class_docstr: Classifier based on BERT implementation.
code: def _set_text_vec(self, *args, **kwargs):
      obs = super()._set_text_vec(*args, **kwargs)
      if "text_vec" in obs and self.add_cls_token:
          # insert [CLS] token
          if "added_start_end_tokens" not in obs:
              # Sometimes the obs is cached (meaning its the same object
              # passed the next time) and if so, we would continually re-add
              # the start/end tokens. So, we need to test if already done
              start_tensor = torch.LongTensor([self.dict.start_idx])
              new_text_vec = torch.cat(
                  [start_tensor, obs["text_vec"]], 0
              )
              obs.force_set("text_vec", new_text_vec)
              obs["added_start_end_tokens"] = True
      return obs
  
imports: import os
  from collections import deque
  from typing import Optional
  import torch
  from parlai.agents.bert_ranker.bert_dictionary import BertDictionaryAgent
  from parlai.agents.bert_ranker.helpers import BertWrapper, MODEL_PATH
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.torch_agent import History
  from parlai.core.torch_classifier_agent import TorchClassifierAgent
  from parlai.utils.io import PathManager
  from parlai.utils.misc import warn_once
  from parlai.zoo.bert.build import download
label_desc: Use image node_id 9 for calling the BertClassifierAgent obj's underlying member method code with example usage: obj._set_text_vec() and returns: obs